ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866910426799800320 |
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| author | Lee, JunKyu Varghese, Blesson Vandierendonck, Hans |
| author_facet | Lee, JunKyu Varghese, Blesson Vandierendonck, Hans |
| contents | This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is designed to select an optimal detector out of a set of detectors in real time without label information to maximize real-time object detection accuracy. ROMA utilizing four YOLOv4 detectors on an NVIDIA Jetson Nano shows real-time accuracy improvements by 4 to 37% for a scenario of dynamically varying video contents and detection latency consisting of MOT17Det and MOT20Det datasets, compared to individual YOLOv4 detectors and two state-of-the-art runtime techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_16083 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy Lee, JunKyu Varghese, Blesson Vandierendonck, Hans Computer Vision and Pattern Recognition This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is designed to select an optimal detector out of a set of detectors in real time without label information to maximize real-time object detection accuracy. ROMA utilizing four YOLOv4 detectors on an NVIDIA Jetson Nano shows real-time accuracy improvements by 4 to 37% for a scenario of dynamically varying video contents and detection latency consisting of MOT17Det and MOT20Det datasets, compared to individual YOLOv4 detectors and two state-of-the-art runtime techniques. |
| title | ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2210.16083 |